Search

BACKGROUND: Brain plasticity evoked by environmental enrichment through early mobilization may improve sensorimotor functions of patients with moderate-to-severe traumatic brain injury (TBI). Increasing evidence also suggests that early mobilization increases verticalization, which is beneficial to TBI patients in critical care. However, there are limited data on early mobilization interventions provided to patients with moderate-to-severe TBI. OBJECTIVE: We investigated the possible enhancing effects of revised progressive early mobilization on functional mobility and the rate of out-of-bed mobility attained by patients with moderate-to-severe TBI. METHODS: This is a quantitative study with a retrospective and prospective pre–post intervention design. We implemented a revised progressive early mobilization protocol for patients with moderate-to-severe TBI admitted to the trauma intensive care unit (ICU) within the previous seven days. The outcome parameters were the rate of patients attaining early mobilization (sitting on the edge of the bed) and the Perme ICU Mobility Score at discharge from the ICU. The outcome parameters in the intervention cohort were compared with those from a historical control cohort who received standard medical care a year previously. Differences in the Perme ICU Mobility Score between the two cohorts were assessed using univariate analysis of covariance. RESULTS: Forty-two patients were included in the progressive early mobilization program and were compared with 44 patients who underwent standard medical care. In the intervention cohort, 100% and 57.2% of the patients completed early rehabilitation and early mobilization, respectively, compared to 0% in the control cohort. The intervention cohort at ICU discharge showed significantly improved the Perme ICU Mobility Scores. CONCLUSIONS: The implementation of the revised progressive early mobilization program for patients with moderate-to-severe TBI resulted in significantly improved mobility at ICU discharge; however, the length of overall stay in the ICU may be not affected.

This paper proposes an intelligent attendance monitoring system based on spatio-temporal human action recognition, which includes human skeleton gait recognition, multi-action body silhouette recognition and face recognition. Our system solves several problems, for example, when a mask is worn to conceal the face, which leads to a decrease in recognition accuracy performance, and when a 3D face mask is used to fake an identity. The skeleton gait feature of our intelligent attendance monitoring system uses a temporal weighted K-nearest neighbours algorithm to train the recognition model and carry out identification, while the multi-action body silhouette feature uses a multiple K-nearest neighbours algorithm to train the recognition model, identify the person and vote on the outcome. Using the proposed system, which integrates skeleton gait features, action silhouette features and face features, more effective recognition can be achieved. When the system encounters a situation with feature masking, such as when an individual is wearing a mask or has changed their clothes, or when the viewing angle is masked, it can continue to deliver good recognition ability through multi-angle skeleton synthesis gait recognition. Our experimental results show that the recognition accuracy of the system is 83.33% when a specific person wears a mask and passes through a monitored area. The intelligent attendance monitoring system uses a LINE messaging API as the access control notification function and provides a responsive web platform that allows managers to perform follow-up management and monitoring.

A monitoring system for smart machinery has been considered to be one of the most important goals in recent enterprises. This monitoring system will encounter huge difficulties, such as more data uploaded by smart machines, and the available internet bandwidth will influence the transmission speed of data and the reliability of the equipment monitoring platform. This paper proposes reducing the periodical information that has been uploaded to the monitoring platform by setting an upload event through the traits of production data from machines. The proposed methods reduce bandwidth and power consumption. The monitoring information is reconstructed by the proposed methods, so history data will not reduce storage in the cloud server database. In order to reduce the halt time caused by machine error, the proposed system uses machine-learning technology to model the operating status of machinery for fault prediction. In the experimental results, the smart machinery monitoring system using the Industrial Internet of Things reduces the volume of information uploaded by 54.57% and obtains a 98% prediction accuracy.

BACKGROUND: Recent advances in technology have warranted the use of wearable sensors to monitor gait and posture. However, the psychometric properties of using wearable devices to measure gait-related outcomes have not been fully established in patients with Parkinson’s disease (PD). OBJECTIVE: This study aimed to investigate the test-retest reliability of body-worn sensors for gait evaluation in people with PD. Additionally, the influence of disease severity on the reliability was determined. METHODS: Twenty individuals with PD were recruited. During the first evaluation, the participants wore inertial sensors on their shoes and walked along a walkway thrice at their comfortable walking speed. The participants were then required to return to the lab after 3–5 days to complete the second evaluation with the same study procedure. Test-retest reliability of gait-related outcomes were calculated. To determine whether the results would be affected by disease severity, reliability was re-calculated by subdividing the participants into early and mid-advanced stages of the disease. RESULTS: The results showed moderate to good reliability (ICC = 0.64–0.87) of the wearable sensors for gait assessment in the general population with PD. Subgroup analysis showed that the reliability was higher among patients at early stages (ICC = 0.71–0.97) compared to those at mid-advanced stages (ICC = 0.65–0.81) of PD. CONCLUSIONS: Wearable sensors could reliably measure gait parameters in people with PD, and the reliability was higher among individuals at early stages of the disease compared to those at mid-advanced stages. Absolute reliability values were calculated to act as references for future studies.

In the original publication, the affiliation of Min-Hao Li was published incorrectly as below.

The dynamic response of an infinitely long circular tunnel embedded in the saturated soil to a series of equidistant moving loads (SEML) is investigated in this study. The saturated soil surrounding the tunnel is described by Biot’s theory. Two scalar potentials and one vector potential are introduced to represent the displacement of the solid skeleton and the pore pressure. Based on Biot’s theory, the Helmholtz equations for the potentials are derived using the Fourier transform method. Performing the Fourier transform with respect to the longitudinal coordinate on the Helmholtz equations gives the frequency–wavenumber domain general solutions for the potentials. With the general solutions and the boundary conditions along the tunnel surface, the solution for the circular tunnel subjected to a single moving load (SML) is obtained. Superposition of the solutions for SMLs gives the representation for the response of the tunnel to a SEML, with which the resonance and cancelation conditions for the SEML are derived. Numerical simulations suggest that for the circular infinite tunnel there is a critical velocity. When the load moves with the critical velocity, the frequency domain response of the tunnel exhibits some dominant peak. Further, for a SEML moving with the critical velocity, the resonance and cancelation phenomena in the time domain may occur when the aforementioned resonance and cancelation conditions are fulfilled.

This study focuses on the construction of a reversible nano reactor based on the thermo-responsive property of polyethylene glycol side chain hanging on the main chain of polystyrene along with quaternary ammonium groups. The polystyrene derivates are obtained by polymerization of vinylbenzyl PEG ether and/or vinylbenzyl quaternary ammonium. The vinylbenzyl PEG ether and vinylbenzyl quaternary ammonium were prepared by the reaction of vinylbenzyl chloride with diol and trimethylamine. The structure of vinylbenzyl derivate was characterized by 1H NMR, FT-IR, UV–vis and Q-TOF mass spectroscopy. The micelle properties of polymers were characterized by UV–vis spectroscopy and optical microscope. The micelle catalytic activities were evaluated using the chloromethylation of naphthalene as the model. The conversion of naphthalene is up to 93% at the optimized reaction condition using the polystyrene with PEG-like side chains and quaternary ammonium P(3-4) as the micelle catalyst. The catalyst can be isolated just by separation of the interlayer between the organic phase and water phase, and recycled six time without significant activity loss.

Recently, the demand for communication has been growing rapidly. Hence, optical multistage network technologies are more appreciated nowadays. A double-layer network is a strictly nonblocking network, and it has the lowest system insertion loss of non-dilated networks. A Benes network is a rearrangeably nonblocking network, and it has the same system insertion loss as a double-layer network. We have proposed the use of modified polarization selection elements (PSEs). The system insertion loss, number of drivers, and number of required components of a double-layer network could be reduced if it is constructed with modified PSEs. A nonblocking 4×4 optical switch with two stages of polarization selective elements has been presented in our previous study. Based on this nonblocking 4×4 optical switch, we propose a strictly nonblocking network structure which features even lower system insertion loss than those of a double-layer network and a Benes network. The signal-to-noise ratio of the proposed network structure is a constant, and is higher than the constraint, although it is lower than that of the double-layer network. The number of major components of the proposed network is less than that of a double-layer network and larger than that of a Benes network, since a Benes network is rearrangeably nonblocking. We also offer a routing algorithm for the new proposed network; the time complexity of the routing algorithm is O ( 1 ) .